In analysis of various categorical variables of interest (e.g., disease state, genotype, phenotype, behaviors of interest, company profitability, and so on), experimental groups may be organized based on the categorical variables of interest. One approach to investigate the features or characteristics that affect or relate to the categorical variable of interest is to classify the individual subjects, objects, or entities into experimental groups based on the variables of interest, and analyze the statistical relationship between features and group membership.
Commonly used approaches to discriminate among experimental groups having multiple features employ supervised classification approaches that require prior knowledge of the features that best discriminate between the groups. Such knowledge is not readily available in behavioral science, so an experimenter's attempts to impose criteria for categorization are prone to error. The ability to achieve classification without reliance on preconceptions enables unbiased identification of the most salient features with high accuracy. Embodiments disclosed herein provide efficient methods and system for classifying subjects and objects, and analyzing features and factors associated variables of interest.